FV3900 - Fire Science Dissertation

Assignment Task

Introduction

Lakanal house fire transpired in Camberwell, London, on 3 July 2009, killing six individuals and leaving at least twenty injured (Knight, 2009). A fault in a TV set on the ninth floor caused the high-rise fire, which spread through many flats in the fourteen-story building (Barling, 2017). What started as a small fire became a layered story covering building regulations, fire safety, the opaqueness of public authorities, and suspicions regarding the role risk of domestic appliances on fire spread. While the cause of the Lakanal house fire was identified, its spread was unusual, leaving many questions unanswered. Besides, the initial investigation officers could not identify factors and mechanisms that led to the fire's development and spread (Knight, 2009). UK's fire policy demanded that fire be contained in a compartment as of the date the Lakanal house fire took place, raising concerns on whether the policy was ineffective in preventing fire development and spread or whether additional factors were involved (Barling, 2017). These questions cover internal passive fire protection and external fire spread.

Case Study

Many studies often research the causes of electrical appliance-related house fires and recommend preventive measures that, despite being helpful, have failed in certain circumstances, as seen in the Lakanal house fire case. Machine learning has received wide acceptance in many areas, and fire safety and protection are no different. This study investigates the feasibility of using machine and deep learning algorithms to learn the performance of domestic appliances and building energy usage patterns, then use the information to predict and stop appliance-related house fires before tripping the main circuit is necessary. Unlike many past studies, this research focuses on determining the loopholes in domestic appliance-related house fire preventative measures. It proposes better approaches worth exploiting, given machine and deep learning algorithms advancements. Besides, it bridges the gap in the literature on the rate of heat release by domestic appliances to help homeowners know how to potion them with other house items in a way that prevents fire development once they ignite.

Research Questions

i. What common electric faults in domestic appliances cause appliance-related house fires?

ii. What is the rate of heat release by appliances leading to domestic house fires?

iii. What measures are instilled in domestic appliances to reduce fire development once they self-ignite?

iv. What is the effectiveness of AFDDs in predicting arc and preventing appliancerelated house fires?

v. Can algorithms be used to prevent domestic appliance-related house fires, and how do algorithms compare to AFDDs when used to prevent appliance-related house fires?

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